Attributing Venice Acqua Alta events to a changing climate and evaluating the efficacy of MoSE adaptation strategy
Bibliographic record
Abstract
We use analogues of atmospheric patterns to investigate changes in the three most devastating Acqua Alta (flooding) events in the lagoon of Venice associated with intense Mediterranean cyclones occurred in 1966, 2018 and 2019. Our results provide evidence that changes in atmospheric circulation, although not necessarily anthropogenically driven only, are linked to the severity of these events. We also evaluate the cost and benefit of the MoSE system, which was designed to protect against flooding. Our analysis shows that the MoSE has already provided protection against analogues of the most extreme events, which occurred in 1966, while for 2018 and 2019 events our analysis is non-conclusive because of the lack of analogues situations of those events.  These findings have significant implications for the future of Venice and other coastal cities facing similar challenges from rising sea levels due to extreme events. Our study represents one of the first examples that goes beyond identifying the circulation drivers of extreme events to quantifying the changes and their impacts. The framework we presented is general and can be applied to other case studies. However, our study also has limitations, including the limited database of sea-level for the past, the limited analogues used, and the fact that we did not use climate models. Despite these limitations, our study provides important insights into the attribution and impacts of extreme events, which are crucial for developing effective mitigation and adaptation strategies. We hope that our work will inspire future research and inform policymakers in their efforts to reduce the risks associated with extreme events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".